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Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data
MOTIVATION: An important goal of concentration–response studies in toxicology is to determine an ‘alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8337003/ https://www.ncbi.nlm.nih.gov/pubmed/33515236 http://dx.doi.org/10.1093/bioinformatics/btab043 |
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author | Kappenberg, Franziska Grinberg, Marianna Jiang, Xiaoqi Kopp-Schneider, Annette Hengstler, Jan G Rahnenführer, Jörg |
author_facet | Kappenberg, Franziska Grinberg, Marianna Jiang, Xiaoqi Kopp-Schneider, Annette Hengstler, Jan G Rahnenführer, Jörg |
author_sort | Kappenberg, Franziska |
collection | PubMed |
description | MOTIVATION: An important goal of concentration–response studies in toxicology is to determine an ‘alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. RESULTS: In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. AVAILABILITY AND IMPLEMENTATION: The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8337003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-83370032021-08-09 Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data Kappenberg, Franziska Grinberg, Marianna Jiang, Xiaoqi Kopp-Schneider, Annette Hengstler, Jan G Rahnenführer, Jörg Bioinformatics Original Papers MOTIVATION: An important goal of concentration–response studies in toxicology is to determine an ‘alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. RESULTS: In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. AVAILABILITY AND IMPLEMENTATION: The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-01-30 /pmc/articles/PMC8337003/ /pubmed/33515236 http://dx.doi.org/10.1093/bioinformatics/btab043 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Kappenberg, Franziska Grinberg, Marianna Jiang, Xiaoqi Kopp-Schneider, Annette Hengstler, Jan G Rahnenführer, Jörg Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data |
title | Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data |
title_full | Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data |
title_fullStr | Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data |
title_full_unstemmed | Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data |
title_short | Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data |
title_sort | comparison of observation-based and model-based identification of alert concentrations from concentration–expression data |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8337003/ https://www.ncbi.nlm.nih.gov/pubmed/33515236 http://dx.doi.org/10.1093/bioinformatics/btab043 |
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